This project contains the jupyter notebook and the data used for the Machine Learning C;lassification problem

In this notebook, I have reviewed several classification algorithms on datasets.

This is a program to predict whether a person is at low or high risk of having a heart attack.

we predict that the patient is suffering with heart attack or not

Mechine Learning | Heart Attack Prediction

Creating machine learning model analysis using logistic regression and run the Streamlit apps to predict the probability of having heart attack in future.

Trying different ML algorithms to predict the chance of getting Heart Attack on basis of given input.

predicting the risk of heart attack using various machine learning models such as Logistic Regression, Decision Tree, Random Forest, K Nearest Neighbour and SVM

Predicting heart attack risk using ML techniques. Final project for DSCI 631.

Halo semua! Ini adalah model klasifikasi menggunakan algoritma GradientBoostingRegressor yang dimana menghasilkan akurasi 99% untuk setiap kelasnya dan menghasilkan evaluasi dari model yang maksimal. Di repo ini juga, terdapat file deployment implementasi kedalam website di Streamlit! jangan lupa untuk follow Github ku ya!

Experimenting with MindsDB & Python Classification Algorithms

Machine Learning Project 🤖 . Much research has been conducted to pinpoint the most powerful factors of heart disease and accurately predict the overall risk. Heart Attack is even highlighted as a silent killer that leads to the person's death without noticeable symptoms. Most heart patients are treated for heart diseases but they are not well co…

Main repository for Kaggle's heart attack data science project (EDA + prediction).

Este proyecto aborda la limpieza, análisis y modelado predictivo de un conjunto de datos de ataques al corazón. Utilizamos métodos de estadística y aprendizaje supervisado para clasificar las probabilidades de sufrir un ataque al corazón en función de varias características clínicas.

Data is analyzed to identify the likelihood of a patient who has had a heart attack, and what their survival rate will be after the event.

classification problem using knn, svm and simple neural networks

CNN heart attack prediction model

Machine learning model to detect a heart attack before they happen.

This project aims to predict the likelihood of a heart attack based on various health indicators using machine learning techniques. The dataset used contains patient data with features such as age, cholesterol levels, blood pressure, and more.